The rapid proliferation of digital images has increased the need to classify images for easier retrieving, reviewing, and albuming of the images. Manual classification is effective, but is slow and burdensome unless the number of images is small. Automated methods are available, but tend to have a number of constraints, such as requiring extensive processing resources. As a result, the suitability of different automated methods tends to depend upon a particular use and type of classification. One type of classification is by event.
Some automated methods partition images into groups having similar image characteristics based upon color, shape or texture. This approach can be used to classify by event, but is inherently difficult when used for that purpose. “Home Photo Content Modeling for Personalized Event-Based Retrieval”, Lim, J-H, et al., IEEE Multimedia, Vol. 10(4), October-December 2003, pages 28-37 discloses classification of images by event using image content.
Many images are accompanied by metadata, that is, associated non-image information, that can be used to help grouping the images. One example of such metadata is chronological data, such as date and time, and geographic data, such as Global Positioning System (“GPS”) geographic position data. These types of data are particularly suitable for grouping by event, since events are limited temporally and usually limited spatially. Users have long grouped images manually by looking at each image and sorting by chronology and geography. The above-cited article by Lim et al., suggests use of chronological and geographic data in automated image classification by event using image content.
Statistical techniques are well known for classifying data using metrics related to variance, such as: standard deviation, variance, mean deviation, and sample variation.
It would thus be desirable to provide simple and efficient image classification using variance-based techniques with grouping data, such as chronological or geographical data.